A sales qualified lead (SQL) is a prospect who has been evaluated by sales and determined to be ready for active pursuit — meaning there is a genuine fit, an identified problem the product solves, sufficient budget, and a realistic likelihood of closing within a defined timeframe. SQL status marks the point at which a lead formally enters the sales pipeline and a sales representative takes ownership of moving the deal forward. The distinction between a marketing qualified lead (MQL) and a SQL captures the difference between “looks promising” and “we are actively trying to close this.”
The SQL concept addresses a specific failure mode in B2B sales: salespeople spending time with prospects who seem interested but will never buy. Without explicit qualification criteria, sales teams often pursue deals based on engagement signals (the prospect took a demo, they are responsive on email) rather than buying signals (they have a real problem, a real budget, and authority to make a decision). SQL criteria enforce the discipline of qualifying before investing significant sales time.
How SQLs Are Defined: BANT and Its Successors
The most widely used framework for SQL qualification is BANT: Budget, Authority, Need, and Timeline. A prospect who can answer yes to all four — they have the budget for a solution like this, they are the decision-maker or significantly influence the decision, they have a problem the product solves, and they have a timeframe for a decision — is a strong SQL candidate. BANT was developed by IBM in the 1950s and remains in widespread use because it maps to the four dimensions that reliably predict whether a deal will close.
More recent qualification frameworks have refined or extended BANT. MEDDIC (Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion) is widely used in enterprise sales because it goes deeper on organizational dynamics — not just “do they have budget” but “who actually controls the budget and what do they care about.” SPIN (Situation, Problem, Implication, Need-Payoff) is a conversational approach to eliciting qualification information through questions rather than a static checklist. The specific framework matters less than the discipline of consistently qualifying before moving a lead into active pipeline.
MQL to SQL: The Qualification Conversation
The transition from MQL to SQL happens through a qualification conversation — typically a discovery call conducted by a sales development representative (SDR) or account executive. The goal of this call is not to pitch the product but to determine whether pursuing the deal is a good use of sales time. An SDR who spends 30 minutes with a prospect, determines they do not have budget for 18 months, and disqualifies the lead has done exactly what the role is designed to do: protect account executive time from prospects who will not close in a reasonable timeframe.
The qualification conversation should establish: the current state of the problem (what is the prospect doing today, what is broken about it), the urgency and business impact (why does this need to change, what happens if they do not address it), the decision process and timeline (who is involved, when do they need to decide), and budget range. Qualification is not about convincing the prospect to buy — that comes later. It is about determining whether there is a real opportunity worth pursuing.
SQL Metrics and Sales Pipeline Health
SQL-to-Close Rate
The SQL-to-close rate (what percentage of SQLs eventually close as won deals) is the primary measure of pipeline quality. A SQL-to-close rate of 20-30% is typical in competitive B2B software markets; rates significantly below this suggest either that qualification criteria are too permissive (the wrong leads are being passed as SQLs) or that sales execution is weak (the right leads are being lost to competitor or no-decision). A rate significantly above 30% may indicate the pipeline is too conservative — good opportunities are being disqualified that could be won.
Average Deal Velocity
Deal velocity — the average time from SQL creation to close — measures how long it takes to move qualified prospects through the pipeline. Long average deal cycles (6-12+ months) may indicate that deals are entering the pipeline too early (before the prospect is truly ready to buy), that the sales process has unnecessary friction or approval steps, or that the product requires significant internal selling on the customer side. Short deal cycles indicate well-qualified buyers with a strong sense of urgency and a streamlined decision process.
SQL Attribution: Connecting Pipeline to Marketing
Attributing SQL creation to specific marketing channels and campaigns answers the question revenue leadership actually cares about: which marketing activities are producing pipeline, not just leads? A content campaign that generates 100 MQLs but 5 SQLs is performing worse than a campaign that generates 30 MQLs and 20 SQLs, even though the first campaign looks better on a top-of-funnel dashboard.
SQL-level attribution requires tracking lead source through the qualification process — recording not just where the MQL first came from but whether that MQL became an SQL and eventually closed. This tracking is typically built in the CRM, where SQL stage entry is a pipeline stage and lead source (or multi-touch attribution data) is a field that persists through the pipeline. Reports that segment SQL volume, SQL-to-close rate, and closed revenue by lead source channel provide the attribution data that makes marketing spending decisions defensible.